In this section, we experimentally evaluate the performance of the BANMF algorithm. For comparison we employ ASSO [1]}, NMF [2]}, and post nonlinear penalty function (PNL-PF) [3]} where the solutions are thresholded using Booleanization alg:booleanization to be binary. First, we demonstrate that if the non-negative rank and the Boolean rank are different, then BANMF, and in particular Regularized BANMF, outperform the other methods. Next, we investigate how methods perform with regard to different noise and density levels. We then show that BANMF outperforms its strongest contender, PNL-PF, in execution time for random matrices. Lastly, the methods are evaluated when applied to three real datasets: (1) UCI Zoo, (2) Congressional voting records, and (3) Lung cancer with probes.
